Frontier Research Overview
How can we develop soft body and brain for intelligent motions and soft interactions?
To address this challenge, we investigate the effect of soft material on control, learning, and adaptation by introducing soft material into the rigid robot body or replacing the rigid parts with soft material. We also aim to identify the balance between the rigidness and softness of robot bodies for robust and versatile behaviors.
2025
Performance consequences of information-based centralization arising from neural and mechanical coupling in a walking robot
Abstract: Legged animals still outperform many terrestrial robots due to the complex interplay of various component subsystems. Centralization is a potential integrated design axis to help improve the performance of legged robots in variable terrain environments. Centralization arises from the coupling of multiple limbs and joints through mechanics or feedback control. Strong couplings contribute to a whole-body coordinated response (centralized) and weak couplings result in localized responses (decentralized). Rarely are both mechanical and neural couplings considered together in designing centralization. In this study, we use an empirical information theory-based approach to evaluate the emergent centralization of a hexapod robot. We independently vary the mechanical and neural coupling through adjustable joint stiffness and variable coupling of leg controllers, respectively. We found an increase in centralization as neural coupling increased. Changes in mechanical coupling did not significantly affect centralization during walking, but did change the total information processing of the neuromechanical control architecture. Information-based centralization increased with robotic performance in terms of cost of transport and speed, implying that this may be a useful metric in robotic design.
Reference: Liu, E., Asawalertsak, N., Sponberg, S., and Manoonpong, P.. Performance consequences of information-based centralization arising from neural and mechanical coupling in a walking robot. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. ). IEEE.
Adaptive Neural Control with Online Learning and Short-Term Memory for Adaptive Soft Crawling Robots
Abstract: Soft-bodied crawling animals exhibit efficient and adaptive behaviors resulting from the synergy between morphological computation (e.g., a flexible soft body and anisotropic skin) and neural computation (e.g., neural control with plasticity and short-term memory (STM)). However, applying these principles to soft crawling robots remains challenging. To address this, our study proposes an adaptive neural control system that incorporates online learning and STM to generate adaptive behaviors in soft crawling robots. This control system was implemented in a robot with a flexible soft body, anisotropic abdominal denticles or skin, and embodied laser and flex sensors. The robot demonstrated a multilevel adaptation to various perturbations. Perturbations, such as rough terrain, can be managed through passive (body) adaptation via micro-deformation of the denticles and macro-deformation of the body. Larger perturbations, including being lifted or pressed, crawling through confined spaces, and traversing slopes, are handled by active (neural control) adaptation. The robot can learn new behaviors, such as crawling through confined spaces, and store sensory information to maintain the learned behavior robustly, even in the temporary absence of sensory feedback. In addition, it can estimate its state through sensory feedback prediction, detect abnormal states through prediction errors, and adapt its behavior to address these errors.
Reference: Asawalertsak, N., & Manoonpong, P. (2025). Adaptive Neural Control with Online Learning and Short-Term Memory for Adaptive Soft Crawling Robots. IEEE Robotics and Automation Letters.
For more details, see Asawalertsak et al., ieee , 2025.
2023
A small soft-bodied crawling robot with electromagnetic legs and neural control for locomotion on various metal terrains
Abstract: Soft-bodied crawling animals (like caterpillars and inchworms) exploit their active soft bodies with passive adaptability to achieve efficient locomotion and move on multiple terrains. While several research studies have used this principle for robot development, most existing caterpillar/inchworm-inspired soft robots can still crawl on specific terrain (flat, inner, or outer pipes). To advance state-of-the-art soft robotic technology, we propose here a small soft-bodied crawling robot with electromagnetic legs and passive body adaptation. The robot is driven by neural central pattern generator (CPG)-based control. Due to the combination of its actively contractable/extendable body, passively adaptable interconnected body joints, and electromagnetic legs, the robot can successfully crawl on a variety of metal terrains, including a flat surface, step, slope, confined space, and an inner (concave surface) and outer (convex surface) pipe in both horizontal and vertical directions. Additionally, it can be steered to navigate through a cluttered environment with obstacles. Using the CPG-based control method, the robot’s locomotion speed can be simply regulated by changing a single CPG-frequency control parameter. This small soft robot has the potential to be employed as a robotic system for inner and outer pipe inspection and confined space exploration in the oil and gas industry.
Reference: Asawalertsak, N., Nantareekurn, W., Manoonpong, P. (2023) A small soft-bodied crawling robot with electromagnetic legs and neural control for locomotion on various metal terrains,the 6th IEEE-RAS International Conference on Soft Robotics (RoboSoft 2023).
For more details, see Asawalertsak et al., RoboSoft , 2023.
Frictional Anisotropic Locomotion and Adaptive Neural Control for a Soft Crawling Robot
Abstract: Crawling animals with bendable soft bodies use the friction anisotropy of their asymmetric body structures to traverse various substrates efficiently. Although the effect of friction anisotropy has been investigated and applied to robot locomotion, the dynamic interactions between soft body bending at different frequencies (low and high), soft asymmetric surface structures at various aspect ratios (low, medium, and high), and different substrates (rough and smooth) have not been studied comprehensively. To address this lack, we developed a simple soft robot model with a bioinspired asymmetric structure (sawtooth) facing the ground. The robot uses only a single source of pressure for its pneumatic actuation. The frequency, teeth aspect ratio, and substrate parameters and the corresponding dynamic interactions were systematically investigated and analyzed. The study findings indicate that the anterior and posterior parts of the structure deform differently during the interaction, generating different frictional forces. In addition, these parts switched their roles dynamically from push to pull and vice versa in various states, resulting in the robot’s emergent locomotion. Finally, autonomous adaptive crawling behavior of the robot was demonstrated using sensor-driven neural control with a miniature laser sensor installed in the anterior part of the robot. The robot successfully adapted its actuation frequency to reduce body bending and crawl through a narrow space, such as a tunnel. The study serves as a stepping stone for developing simple soft crawling robots capable of navigating cluttered and confined spaces autonomously.
Reference: Asawalertsak, N., Heims, F., Kovalev, A., Gorb, S. N., Jørgensen, J., & Manoonpong, P. (2023). Frictional anisotropic locomotion and adaptive neural control for a soft crawling robot. Soft Robotics, 10(3), 545-555.
For more details, see Asawalertsak et al., Soft Robotics, 2023.
2021
VENOM: Versatile, Adhesive, and Soft Material for Various Surface Adhesion
Abstract: We propose for the first time a versatile, adhesive, and soft material for various surface adhesion (VENOM). It has been developed based on a mixture of super-soft, fast cure platinum-catalyzed silicone (Ecoflex 00-10) and iron powder. Through our empirical investigation, we realized that VENOM with iron powder in the amount of 30% by weight of silicone produced the highest dynamic friction and normal adhesion forces compared to other ratios. We compared the performance of VENOM with other soft (sticky) materials (e.g., other silicone types and non-directional gecko-inspired material) on smooth and rough surfaces. Our experimental results show that VENOM overall has an excellent performance. It has the highest dynamic friction and adhesion forces on both surfaces, except in the case of smooth surfaces where the gecko-inspired material has the highest normal adhesion force. Although the adhesion force of VENOM on smooth surfaces is lower than the gecko-inspired material, several tests show that it has a lower variance. This suggests that VENOM has better repeatable adhesive performance and durability. We believe that this simple, low-cost, dry adhesive material will be beneficial in many aspects of (soft) robotics as well as climbing robot development.
Reference: Suthisomboon, T., Rukpanich, T., Asawalertsak, N., Borijindakul, P., Ji, A., Dai, Z., & Manoonpong, P. (2021, April). Venom: versatile, adhesive, and soft material for various surface adhesion. In 2021 IEEE 4th International Conference on Soft Robotics (RoboSoft) (pp. 543-546). IEEE.